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github/prd

ソフトウェアシステムやAI機能向けの高品質な製品要件定義書(PRD)を生成します。エグゼクティブサマリー、ユーザーストーリー、技術仕様、リスク分析を含みます。

prd とは?

github/prd スキルは、ソフトウェアシステムやAI機能向けの包括的な製品要件定義書(PRD)を自動生成する専門スキルです。開発者やプロダクトマネージャーが手動でPRDを作成する手間を省き、一貫性のある高品質なドキュメントを迅速に作成できます。このスキルは、Claude Code などのAIエージェントプラットフォームで利用可能で、与えられたプロジェクトの概要や要件から、エグゼクティブサマリー、ユーザーストーリー、技術仕様、リスク分析など、PRDに必要なすべてのセクションを生成します。スキルの動作は、プロンプトで提供された情報を解析し、標準的なPRDテンプレートに基づいて構造化されたドキュメントを出力することで実現されています。これにより、プロジェクト開始時の要件定義プロセスを効率化し、関係者間の認識合わせを容易にします。

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/prd

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このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

ドキュメント

prd は何をしますか?

ソフトウェアシステムやAI機能向けの高品質な製品要件定義書(PRD)を生成します。エグゼクティブサマリー、ユーザーストーリー、技術仕様、リスク分析を含みます。

使用方法

このスキルは、以下のようなプロンプトで呼び出せます:

/product-requirements-document [プロジェクトの説明や要件]

または会話の中で直接 PRD の作成を依頼することもできます。

生成されるセクション

  • エグゼクティブサマリー: プロジェクトの背景、目標、主要な成果物を簡潔にまとめます。
  • ユーザーストーリー: 主要なユーザーとそのニーズに基づいたストーリーを記述します。
  • 技術仕様: システムのアーキテクチャ、コンポーネント、API、データモデルなどを定義します。
  • リスク分析: 潜在的なリスクとその軽減策を特定します。

入力例

/product-requirements-document
AI駆動のコードレビューツールを作成します。主な機能は自動レビュー、スタイルチェック、セキュリティ脆弱性の検出です。

出力例

上記の入力に対し、スキルは完全なPRDを以下のような構造で生成します:

  • エグゼクティブサマリー
  • ユーザーストーリー(開発者、チームリーダー、セキュリティエンジニア向け)
  • 技術仕様(アーキテクチャ図、APIエンドポイント、データベーススキーマ)
  • リスク分析(誤検出のリスク、性能問題、プライバシー懸念)

対応プラットフォーム

  • Claude Code
  • その他のAIエージェント(プロンプトにより適応可能)

注意事項

  • プロジェクトの詳細を具体的に提供することで、より適切なPRDが生成されます。
  • 生成されたPRDは設計の出発点として利用し、必要に応じてチームで調整してください。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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